I have a kalman filter like set up, when I get the current value of an observable process, and update my estimate of the state variable with it.
However, my observations are non-uniform in time, and I am trying to add the logic, that if the last observation was really long ago, I should forget about it, and use an a priori estimate X.
I can just switch to X, if time since the last observation is > T. But I was hoping to put in some general setting, which will be consistent with the Kalman filter logic. Does anyone have suggestions/references for it?
Many thanks.